{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [Basic regression: Predict fuel efficiency](https://www.tensorflow.org/tutorials/keras/regression?hl=zh-cn)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用 seaborn 绘制矩阵图 (pairplot)\n",
    "# !pip install -q seaborn==0.12.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.5.0\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pathlib\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data\n",
      "  24576/Unknown - 1s 34us/step"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'/root/.keras/datasets/auto-mpg.data'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_path = keras.utils.get_file(\"auto-mpg.data\", \n",
    "                                    \"http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data\")\n",
    "dataset_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MPG</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Acceleration</th>\n",
       "      <th>Model Year</th>\n",
       "      <th>Origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>2790.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2130.0</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>2295.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>2625.0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2720.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MPG  Cylinders  Displacement  Horsepower  Weight  Acceleration  \\\n",
       "393  27.0          4         140.0        86.0  2790.0          15.6   \n",
       "394  44.0          4          97.0        52.0  2130.0          24.6   \n",
       "395  32.0          4         135.0        84.0  2295.0          11.6   \n",
       "396  28.0          4         120.0        79.0  2625.0          18.6   \n",
       "397  31.0          4         119.0        82.0  2720.0          19.4   \n",
       "\n",
       "     Model Year  Origin  \n",
       "393          82       1  \n",
       "394          82       2  \n",
       "395          82       1  \n",
       "396          82       1  \n",
       "397          82       1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names = ['MPG','Cylinders','Displacement','Horsepower','Weight',\n",
    "                'Acceleration', 'Model Year', 'Origin']\n",
    "raw_dataset = pd.read_csv(dataset_path, names=column_names,\n",
    "                      na_values = \"?\", comment='\\t',\n",
    "                      sep=\" \", skipinitialspace=True)\n",
    "\n",
    "dataset = raw_dataset.copy()\n",
    "dataset.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MPG             0\n",
       "Cylinders       0\n",
       "Displacement    0\n",
       "Horsepower      6\n",
       "Weight          0\n",
       "Acceleration    0\n",
       "Model Year      0\n",
       "Origin          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "origin = dataset.pop('Origin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MPG</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Acceleration</th>\n",
       "      <th>Model Year</th>\n",
       "      <th>USA</th>\n",
       "      <th>Europe</th>\n",
       "      <th>Japan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>27.0</td>\n",
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       "    <tr>\n",
       "      <th>394</th>\n",
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       "      <td>97.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2130.0</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>2295.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>2625.0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2720.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MPG  Cylinders  Displacement  Horsepower  Weight  Acceleration  \\\n",
       "393  27.0          4         140.0        86.0  2790.0          15.6   \n",
       "394  44.0          4          97.0        52.0  2130.0          24.6   \n",
       "395  32.0          4         135.0        84.0  2295.0          11.6   \n",
       "396  28.0          4         120.0        79.0  2625.0          18.6   \n",
       "397  31.0          4         119.0        82.0  2720.0          19.4   \n",
       "\n",
       "     Model Year  USA  Europe  Japan  \n",
       "393          82  1.0     0.0    0.0  \n",
       "394          82  0.0     1.0    0.0  \n",
       "395          82  1.0     0.0    0.0  \n",
       "396          82  1.0     0.0    0.0  \n",
       "397          82  1.0     0.0    0.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['USA'] = (origin == 1)*1.0\n",
    "dataset['Europe'] = (origin == 2)*1.0\n",
    "dataset['Japan'] = (origin == 3)*1.0\n",
    "dataset.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = dataset.sample(frac=0.8,random_state=0)\n",
    "test_dataset = dataset.drop(train_dataset.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f4f18d8b820>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(train_dataset[[\"MPG\", \"Cylinders\", \"Displacement\", \"Weight\"]], diag_kind=\"kde\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cylinders</th>\n",
       "      <td>314.0</td>\n",
       "      <td>5.477707</td>\n",
       "      <td>1.699788</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Displacement</th>\n",
       "      <td>314.0</td>\n",
       "      <td>195.318471</td>\n",
       "      <td>104.331589</td>\n",
       "      <td>68.0</td>\n",
       "      <td>105.50</td>\n",
       "      <td>151.0</td>\n",
       "      <td>265.75</td>\n",
       "      <td>455.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Horsepower</th>\n",
       "      <td>314.0</td>\n",
       "      <td>104.869427</td>\n",
       "      <td>38.096214</td>\n",
       "      <td>46.0</td>\n",
       "      <td>76.25</td>\n",
       "      <td>94.5</td>\n",
       "      <td>128.00</td>\n",
       "      <td>225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight</th>\n",
       "      <td>314.0</td>\n",
       "      <td>2990.251592</td>\n",
       "      <td>843.898596</td>\n",
       "      <td>1649.0</td>\n",
       "      <td>2256.50</td>\n",
       "      <td>2822.5</td>\n",
       "      <td>3608.00</td>\n",
       "      <td>5140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acceleration</th>\n",
       "      <td>314.0</td>\n",
       "      <td>15.559236</td>\n",
       "      <td>2.789230</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.80</td>\n",
       "      <td>15.5</td>\n",
       "      <td>17.20</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model Year</th>\n",
       "      <td>314.0</td>\n",
       "      <td>75.898089</td>\n",
       "      <td>3.675642</td>\n",
       "      <td>70.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>76.0</td>\n",
       "      <td>79.00</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>314.0</td>\n",
       "      <td>0.624204</td>\n",
       "      <td>0.485101</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>314.0</td>\n",
       "      <td>0.178344</td>\n",
       "      <td>0.383413</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>314.0</td>\n",
       "      <td>0.197452</td>\n",
       "      <td>0.398712</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count         mean         std     min      25%     50%  \\\n",
       "Cylinders     314.0     5.477707    1.699788     3.0     4.00     4.0   \n",
       "Displacement  314.0   195.318471  104.331589    68.0   105.50   151.0   \n",
       "Horsepower    314.0   104.869427   38.096214    46.0    76.25    94.5   \n",
       "Weight        314.0  2990.251592  843.898596  1649.0  2256.50  2822.5   \n",
       "Acceleration  314.0    15.559236    2.789230     8.0    13.80    15.5   \n",
       "Model Year    314.0    75.898089    3.675642    70.0    73.00    76.0   \n",
       "USA           314.0     0.624204    0.485101     0.0     0.00     1.0   \n",
       "Europe        314.0     0.178344    0.383413     0.0     0.00     0.0   \n",
       "Japan         314.0     0.197452    0.398712     0.0     0.00     0.0   \n",
       "\n",
       "                  75%     max  \n",
       "Cylinders        8.00     8.0  \n",
       "Displacement   265.75   455.0  \n",
       "Horsepower     128.00   225.0  \n",
       "Weight        3608.00  5140.0  \n",
       "Acceleration    17.20    24.8  \n",
       "Model Year      79.00    82.0  \n",
       "USA              1.00     1.0  \n",
       "Europe           0.00     1.0  \n",
       "Japan            0.00     1.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_stats = train_dataset.describe()\n",
    "train_stats.pop(\"MPG\")\n",
    "train_stats = train_stats.transpose()\n",
    "train_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_labels = train_dataset.pop('MPG')\n",
    "test_labels = test_dataset.pop('MPG')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def norm(x):\n",
    "  return (x - train_stats['mean']) / train_stats['std']\n",
    "normed_train_data = norm(train_dataset)\n",
    "normed_test_data = norm(test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model():\n",
    "  model = keras.Sequential([\n",
    "    layers.Dense(64, activation='relu', input_shape=[len(train_dataset.keys())]),\n",
    "    layers.Dense(64, activation='relu'),\n",
    "    layers.Dense(1)\n",
    "  ])\n",
    "\n",
    "  optimizer = tf.keras.optimizers.RMSprop(0.001)\n",
    "\n",
    "  model.compile(loss='mse',\n",
    "                optimizer=optimizer,\n",
    "                metrics=['mae', 'mse'])\n",
    "  return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = build_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 64)                640       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 4,865\n",
      "Trainable params: 4,865\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.30673933],\n",
       "       [ 0.04709022],\n",
       "       [-0.12441409],\n",
       "       [-0.03196745],\n",
       "       [-0.64782953],\n",
       "       [-0.08759505],\n",
       "       [-0.6667236 ],\n",
       "       [-0.31737792],\n",
       "       [ 0.00150914],\n",
       "       [-0.3877874 ]], dtype=float32)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_batch = normed_train_data[:10]\n",
    "example_result = model.predict(example_batch)\n",
    "example_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "...................................................................................................."
     ]
    }
   ],
   "source": [
    "# 通过为每个完成的时期打印一个点来显示训练进度\n",
    "class PrintDot(keras.callbacks.Callback):\n",
    "  def on_epoch_end(self, epoch, logs):\n",
    "    if epoch % 100 == 0: print('')\n",
    "    print('.', end='')\n",
    "\n",
    "EPOCHS = 1000\n",
    "\n",
    "history = model.fit(\n",
    "  normed_train_data, train_labels,\n",
    "  epochs=EPOCHS, validation_split = 0.2, verbose=0,\n",
    "  callbacks=[PrintDot()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loss</th>\n",
       "      <th>mae</th>\n",
       "      <th>mse</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_mae</th>\n",
       "      <th>val_mse</th>\n",
       "      <th>epoch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>1.938637</td>\n",
       "      <td>0.881305</td>\n",
       "      <td>1.938637</td>\n",
       "      <td>9.324660</td>\n",
       "      <td>2.443405</td>\n",
       "      <td>9.324660</td>\n",
       "      <td>995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>1.765415</td>\n",
       "      <td>0.859931</td>\n",
       "      <td>1.765415</td>\n",
       "      <td>10.270989</td>\n",
       "      <td>2.401720</td>\n",
       "      <td>10.270989</td>\n",
       "      <td>996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>2.078761</td>\n",
       "      <td>0.925779</td>\n",
       "      <td>2.078761</td>\n",
       "      <td>8.835543</td>\n",
       "      <td>2.353587</td>\n",
       "      <td>8.835543</td>\n",
       "      <td>997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>1.841418</td>\n",
       "      <td>0.837594</td>\n",
       "      <td>1.841418</td>\n",
       "      <td>9.314805</td>\n",
       "      <td>2.416085</td>\n",
       "      <td>9.314805</td>\n",
       "      <td>998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>2.006969</td>\n",
       "      <td>0.947890</td>\n",
       "      <td>2.006969</td>\n",
       "      <td>9.028232</td>\n",
       "      <td>2.389908</td>\n",
       "      <td>9.028232</td>\n",
       "      <td>999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         loss       mae       mse   val_loss   val_mae    val_mse  epoch\n",
       "995  1.938637  0.881305  1.938637   9.324660  2.443405   9.324660    995\n",
       "996  1.765415  0.859931  1.765415  10.270989  2.401720  10.270989    996\n",
       "997  2.078761  0.925779  2.078761   8.835543  2.353587   8.835543    997\n",
       "998  1.841418  0.837594  1.841418   9.314805  2.416085   9.314805    998\n",
       "999  2.006969  0.947890  2.006969   9.028232  2.389908   9.028232    999"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist = pd.DataFrame(history.history)\n",
    "hist['epoch'] = history.epoch\n",
    "hist.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_history(history):\n",
    "  hist = pd.DataFrame(history.history)\n",
    "  hist['epoch'] = history.epoch\n",
    "\n",
    "  plt.figure()\n",
    "  plt.xlabel('Epoch')\n",
    "  plt.ylabel('Mean Abs Error [MPG]')\n",
    "  plt.plot(hist['epoch'], hist['mae'],\n",
    "           label='Train Error')\n",
    "  plt.plot(hist['epoch'], hist['val_mae'],\n",
    "           label = 'Val Error')\n",
    "  plt.ylim([0,5])\n",
    "  plt.legend()\n",
    "\n",
    "  plt.figure()\n",
    "  plt.xlabel('Epoch')\n",
    "  plt.ylabel('Mean Square Error [$MPG^2$]')\n",
    "  plt.plot(hist['epoch'], hist['mse'],\n",
    "           label='Train Error')\n",
    "  plt.plot(hist['epoch'], hist['val_mse'],\n",
    "           label = 'Val Error')\n",
    "  plt.ylim([0,20])\n",
    "  plt.legend()\n",
    "  plt.show()\n",
    "\n",
    "\n",
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ".................................................."
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = build_model()\n",
    "\n",
    "# patience 值用来检查改进 epochs 的数量\n",
    "early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)\n",
    "\n",
    "history = model.fit(normed_train_data, train_labels, epochs=EPOCHS,\n",
    "                    validation_split = 0.2, verbose=0, callbacks=[early_stop, PrintDot()])\n",
    "\n",
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 - 0s - loss: 6.2035 - mae: 1.8691 - mse: 6.2035\n",
      "Testing set Mean Abs Error:  1.87 MPG\n"
     ]
    }
   ],
   "source": [
    "loss, mae, mse = model.evaluate(normed_test_data, test_labels, verbose=2)\n",
    "\n",
    "print(\"Testing set Mean Abs Error: {:5.2f} MPG\".format(mae))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_predictions = model.predict(normed_test_data).flatten()\n",
    "\n",
    "plt.scatter(test_labels, test_predictions)\n",
    "plt.xlabel('True Values [MPG]')\n",
    "plt.ylabel('Predictions [MPG]')\n",
    "plt.axis('equal')\n",
    "plt.axis('square')\n",
    "plt.xlim([0,plt.xlim()[1]])\n",
    "plt.ylim([0,plt.ylim()[1]])\n",
    "_ = plt.plot([-100, 100], [-100, 100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "error = test_predictions - test_labels\n",
    "plt.hist(error, bins = 25)\n",
    "plt.xlabel(\"Prediction Error [MPG]\")\n",
    "_ = plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
